\u27Omic\u27 Evaluation of the Region Specific Changes Induced by Non-Cholinergic Diisopropylfluorophosphate (DFP) Exposure in Fischer 344 Rat Brain

Organophosphorous compounds (OPs) are a class of serine esterase inhibitors that have widespread application as pesticides, veterinary pharmaceuticals and chemical warfare agents. Environmental contamination is ubiquitous. The threat of exposure is a concern for both military and civilian populations. Acute inhibition of acetylcholinesterase by OPs triggers a cholinergic crisis that results in muscle flaccidity, paralysis, convulsions and death. At low doses OPs can alter neuronal differentiation, cell signaling, behavior and cognition through unknown mechanisms. An imbalance of reactive oxygen species may be implicated in the adverse effects of OPs. An integrated approach using both metabolomic and transcriptomic techniques was used to reveal some of the non-cholinergic effects of diisopropylfluorophosphate (DFP), a model OP, in rat brain. Adult male Fischer 344 rats were administered 1 mg/kg DFP or saline via subcutaneous injection at 10 mL/kg. Cortex, brainstem, cerebellum and hippocampus were collected at multiple time points ranging from 0.5 - 48 hr. Total RNA was isolated from each region for differential gene expression analysis using the Affymetrix 1.0 ST gene array at 1 hr post dose. Lipid and aqueous extracts were prepared from each brain region at 2 hr post dose, and profiles of small molecule metabolites, lipids and phospholipids were measured using multinuclear NMR spectroscopy. Because the dose was below the threshold for cholinergic toxicity, it was hypothesized that DFP exposure would up-regulate inflammatory pathways and down-regulate processes that result in cellular degradation, such as apoptosis, and that these changes would correlate with perturbations in the small molecule and lipid profiles as well as gene expression. All brain regions reached minimum acetylcholinesterase activity (40-55%) at 1-2 hr post dose with the exception of cortex, which had minimum activity at 12 hr post dose. No brain region showed significant increases in lipid peroxidation. After 1 hr, pathways associated with prostaglandin D2 synthesis were up-regulated in cortex. Brainstem showed increased expression of genes associated with an inflammatory response and ascorbate transport. Cortex showed the most changes in the lipid profile with significant decreases in phosphatidylcholine, phosphatidylethanolamine, cholesterol, n3 and n6 fatty acids. The mitochondrial phospholipid cardiolipin was significantly decreased after 2 hr in brainstem. By evaluating the impact of low level OP exposure on the neuronal phenotype of specific brain regions, we hope to gain a greater understanding of the non-cholinergic mechanisms of action and sensitive target areas in order to improve the development of therapeutic targets for individuals exposed to OPs

Localized Deconvolution: Characterizing NMR-Based Metabolomics Spectroscopic Data using Localized High-Throughput Deconvolution

The interpretation of nuclear magnetic resonance (NMR) experimental results for metabolomics studies requires intensive signal processing and multivariate data analysis techniques. Standard quantification techniques attempt to minimize effects from variations in peak positions caused by sample pH, ionic strength, and composition. These techniques fail to account for adjacent signals which can lead to drastic quantification errors. Attempts at full spectrum deconvolution have been limited in adoption and development due to the computational resources required. Herein, we develop a novel localized deconvolution algorithm for general purpose quantification of NMR-based metabolomics studies. Localized deconvolution decreases average absolute quantification error by 97% and average relative quantification error by 88%. When applied to a 1H metabolomics study, the cross-validation metric, Q2, improved 16% by reducing within group variability. This increase in accuracy leads to additional computing costs that are overcome by translating the algorithm to the mapreduce design paradigm

A Generalized Model for Metabolomic Analysis: Application to Dose and Time Dependent Toxicity

As metabolomic technology expands, validated techniques for analyzing highly dimensional categorical data are becoming increasingly important. This manuscript presents a novel latent vector-based methodology for analyzing complex data sets with multiple groups that include both high and low doses using orthogonal projections to latent structures (OPLS) coupled with hierarchical clustering. This general methodology allows complex experimental designs (e.g., multiple dose and time combinations) to be encoded and directly compared. Further, it allows for the inclusion of low dose samples that do not exhibit a strong enough individual response to be modeled independently. A dose- and time-responsive metabolomic study was completed to evaluate and demonstrate this methodology. Single doses (0.1–100 mg/kg body weight) of α-naphthylisothiocyanate (ANIT), a common model of hepatic cholestasis, were administered orally in corn oil to male Fischer 344 rats. Urine samples were collected pre-dose and daily through day-4 post-dose. Blood samples were collected pre and post-dose to assess indices of clinical toxicity. Urine samples were analyzed by 1H-NMR spectroscopy, and the spectra were adaptively binned to reduce dimensionality. The proposed methodology for NMR-based urinary metabolomics was sensitive enough to detect ANIT-induced effects with respect to both dose and time at doses below the threshold of clinical toxicity. A pattern of ANIT-dependent effects established at the highest dose was seen in the 50 and 20 mg/kg dose groups, an effect not directly identifiable with individual principal component analysis (PCA). Coupling the pattern found by the OPLS algorithm and hierarchical clustering revealed a relationship between the 100, 50 and 20 mg/kg dose groups, suggesting a characteristic effect of ANIT exposure. These studies demonstrate that the use of a metabolomics approach with flexible binning of 1H spectra and appropriate application of multivariate analyses can reveal biologically relevant information about the temporal metabolic perturbations caused by exposure and toxicity

A Generalized Model for Metabolomic Analysis: Application to Dose and Time Dependent Toxicity

As metabolomic technology expands, validated techniques for analyzing highly dimensional categorical data are becoming increasingly important. This manuscript presents a novel latent vector-based methodology for analyzing complex data sets with multiple groups that include both high and low doses using orthogonal projections to latent structures (OPLS) coupled with hierarchical clustering. This general methodology allows complex experimental designs (e.g., multiple dose and time combinations) to be encoded and directly compared. Further, it allows for the inclusion of low dose samples that do not exhibit a strong enough individual response to be modeled independently. A dose- and time-responsive metabolomic study was completed to evaluate and demonstrate this methodology. Single doses (0.1–100 mg/kg body weight) of α-naphthylisothiocyanate (ANIT), a common model of hepatic cholestasis, were administered orally in corn oil to male Fischer 344 rats. Urine samples were collected pre-dose and daily through day-4 post-dose. Blood samples were collected pre and post-dose to assess indices of clinical toxicity. Urine samples were analyzed by 1H-NMR spectroscopy, and the spectra were adaptively binned to reduce dimensionality. The proposed methodology for NMR-based urinary metabolomics was sensitive enough to detect ANIT-induced effects with respect to both dose and time at doses below the threshold of clinical toxicity. A pattern of ANIT-dependent effects established at the highest dose was seen in the 50 and 20 mg/kg dose groups, an effect not directly identifiable with individual principal component analysis (PCA). Coupling the pattern found by the OPLS algorithm and hierarchical clustering revealed a relationship between the 100, 50 and 20 mg/kg dose groups, suggesting a characteristic effect of ANIT exposure. These studies demonstrate that the use of a metabolomics approach with flexible binning of 1H spectra and appropriate application of multivariate analyses can reveal biologically relevant information about the temporal metabolic perturbations caused by exposure and toxicity

Dynamic Adaptive Binning: an Improved Quantification Technique for NMR Spectroscopic Data

The interpretation of nuclear magnetic resonance (NMR) experimental results for metabolomics studies requires intensive signal processing and multivariate data analysis techniques. A key step in this process is the quantification of spectral features, which is commonly accomplished by dividing an NMR spectrum into several hundred integral regions or bins. Binning attempts to minimize effects from variations in peak positions caused by sample pH, ionic strength, and composition, while reducing the dimensionality for multivariate statistical analyses. Herein we develop an improved novel spectral quantification technique, dynamic adaptive binning. With this technique, bin boundaries are determined by optimizing an objective function using a dynamic programming strategy. The objective function measures the quality of a bin configuration based on the number of peaks per bin. This technique shows a significant improvement over both traditional uniform binning and other adaptive binning techniques. This improvement is quantified via synthetic validation sets by analyzing an algorithm’s ability to create bins that do not contain more than a single peak and that maximize the distance from peak to bin boundary. The validation sets are developed by characterizing the salient distributions in experimental NMR spectroscopic data. Further, dynamic adaptive binning is applied to a 1H NMR-based experiment to monitor rat urinary metabolites to empirically demonstrate improved spectral quantification

Dynamic Adaptive Binning: an Improved Quantification Technique for NMR Spectroscopic Data

The interpretation of nuclear magnetic resonance (NMR) experimental results for metabolomics studies requires intensive signal processing and multivariate data analysis techniques. A key step in this process is the quantification of spectral features, which is commonly accomplished by dividing an NMR spectrum into several hundred integral regions or bins. Binning attempts to minimize effects from variations in peak positions caused by sample pH, ionic strength, and composition, while reducing the dimensionality for multivariate statistical analyses. Herein we develop an improved novel spectral quantification technique, dynamic adaptive binning. With this technique, bin boundaries are determined by optimizing an objective function using a dynamic programming strategy. The objective function measures the quality of a bin configuration based on the number of peaks per bin. This technique shows a significant improvement over both traditional uniform binning and other adaptive binning techniques. This improvement is quantified via synthetic validation sets by analyzing an algorithm’s ability to create bins that do not contain more than a single peak and that maximize the distance from peak to bin boundary. The validation sets are developed by characterizing the salient distributions in experimental NMR spectroscopic data. Further, dynamic adaptive binning is applied to a 1H NMR-based experiment to monitor rat urinary metabolites to empirically demonstrate improved spectral quantification

Dynamic Adaptive Binning: an Improved Quantification Technique for NMR Spectroscopic Data

The interpretation of nuclear magnetic resonance (NMR) experimental results for metabolomics studies requires intensive signal processing and multivariate data analysis techniques. A key step in this process is the quantification of spectral features, which is commonly accomplished by dividing an NMR spectrum into several hundred integral regions or bins. Binning attempts to minimize effects from variations in peak positions caused by sample pH, ionic strength, and composition, while reducing the dimensionality for multivariate statistical analyses. Herein we develop an improved novel spectral quantification technique, dynamic adaptive binning. With this technique, bin boundaries are determined by optimizing an objective function using a dynamic programming strategy. The objective function measures the quality of a bin configuration based on the number of peaks per bin. This technique shows a significant improvement over both traditional uniform binning and other adaptive binning techniques. This improvement is quantified via synthetic validation sets by analyzing an algorithm’s ability to create bins that do not contain more than a single peak and that maximize the distance from peak to bin boundary. The validation sets are developed by characterizing the salient distributions in experimental NMR spectroscopic data. Further, dynamic adaptive binning is applied to a 1H NMR-based experiment to monitor rat urinary metabolites to empirically demonstrate improved spectral quantification

BIOMARKERS OF FATIGUE: Metabolomics Profiles Predictive of Cognitive Performance

Cognitive performance and fatigue are well known to be inversely related. Continuous and sustained actions in operational environments typically lead to reduced sleep normally required to perform optimally. These operational environments subject the warfighter to intense physical and mental exertion. Because fatigue continues to be an occupational hazard, leading to cognitive defects in performance, there has been a recognized need for real-time detection technologies that minimize fatigue-induced mishaps. I the current study, 23 subjects were subjected to 36h of sleep deprivation and cognitive psychomotor vigilance and automated neuropsychological assessment metric tests were conducted over the last 24 h of sleep deprivation. In addition, urine was collected prior to and over the course of the cognitive testing period for metabolite analysis using nuclear magnetic resonance (NMR) spectroscopy. Bioinformatics analysis of the NMR data identified 23 spectral resonances associated with specific urinary metabolites that could be used to classify subject fatigue susceptibility 12 h prior to cognitive testing and at 28 h of sleep deprivation on cognitive testing. Of these, 14 were found to statistically significant when associated with testing cognitive performance. A majority of these metabolites appeared to be associated with nutritional status and suggested that observed increases in dietary protein intake prior to cognitive testing led to increased cognitive performance when sleep deprived. NMR data were also found to correlate with previously reported psychological testing results of these same subjects. Taken together, our results indicate that a subset of urinary metabolites may provide a useful noninvasive biomarker screen for mission performance and readiness during sustained, demeaning missions